OpenRaiser/Intern-Atlas huggingface dataset:
Sliceable Monolith: Monolith First, Microservices Later
Venue: arXiv cs.PL 2021
Year: 2021

We propose Sliceable Monolith, a new methodology for developing microservice architectures and perform their integration testing by leveraging most of the simplicity of a monolith: a single codebase and a local execution environment that simulates distribution. Then, a tool compiles a codebase for each microservice and a cloud deployment configuration. The key enabler of our approach is the technology-agnostic service definition language offered by Jolie.
Investigating Monolingual and Multilingual BERTModels for Vietnamese Aspect Category Detection
Venue: arXiv cs.CL 2021
Year: 2021

Aspect category detection (ACD) is one of the challenging tasks in the Aspect-based sentiment Analysis problem. The purpose of this task is to identify the aspect categories mentioned in user-generated reviews from a set of pre-defined categories. In this paper, we investigate the performance of various monolingual pre-trained language models compared with multilingual models on the Vietnamese aspect category detection problem. We conduct the experiments on two benchmark datasets for the restaurant and hotel domain. The experimental results demonstrated the effectiveness of the monolingual PhoBERT model than others on two datasets. We also evaluate the performance of the multilingual model based on the combination of whole SemEval-2016 datasets in other languages with the Vietnamese dataset. To the best of our knowledge, our research study is the first attempt at performing various available pre-trained language models on aspect category detection task and utilize the datasets from other languages based on multilingual models.
Multi-channel Deep Supervision for Crowd Counting
Venue: arXiv cs.CV 2021
Year: 2021

Crowd counting is a task worth exploring in modern society because of its wide applications such as public safety and video monitoring. Many CNN-based approaches have been proposed to improve the accuracy of estimation, but there are some inherent issues affect the performance, such as overfitting and details lost caused by pooling layers. To tackle these problems, in this paper, we propose an effective network called MDSNet, which introduces a novel supervision framework called Multi-channel Deep Supervision (MDS). The MDS conducts channel-wise supervision on the decoder of the estimation model to help generate the density maps. To obtain the accurate supervision information of different channels, the MDSNet employs an auxiliary network called SupervisionNet (SN) to generate abundant supervision maps based on existing groundtruth. Besides the traditional density map supervision, we also use the SN to convert the dot annotations into continuous supervision information and conduct dot supervision in the MDSNet. Extensive experiments on several mainstream benchmarks show that the proposed MDSNet achieves competitive results and the MDS significantly improves the performance without changing the network structure.
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Venue: arXiv cs.AI 2021
Year: 2021

Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems.
UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words Model
Venue: arXiv cs.CL 2021
Year: 2021

With the ever-increasing availability of digital information, toxic content is also on the rise. Therefore, the detection of this type of language is of paramount importance. We tackle this problem utilizing a combination of a state-of-the-art pre-trained language model (CharacterBERT) and a traditional bag-of-words technique. Since the content is full of toxic words that have not been written according to their dictionary spelling, attendance to individual characters is crucial. Therefore, we use CharacterBERT to extract features based on the word characters. It consists of a CharacterCNN module that learns character embeddings from the context. These are, then, fed into the well-known BERT architecture. The bag-of-words method, on the other hand, further improves upon that by making sure that some frequently used toxic words get labeled accordingly. With a 4 percent difference from the first team, our system ranked 36th in the competition. The code is available for further re-search and reproduction of the results.
An open-source ABAQUS implementation of the scaled boundary finite element method to study interfacial problems using polyhedral meshes
Venue: arXiv cs.MS 2021
Year: 2021

The scaled boundary finite element method (SBFEM) is capable of generating polyhedral elements with an arbitrary number of surfaces. This salient feature significantly alleviates the meshing burden being a bottleneck in the analysis pipeline in the standard finite element method (FEM). In this paper, we implement polyhedral elements based on the SBFEM into the commercial finite element software ABAQUS. To this end, user elements are provided through the user subroutine UEL. Detailed explanations regarding the data structures and implementational aspects of the procedures are given. The focus of the current implementation is on interfacial problems and therefore, element-based surfaces are created on polyhedral user elements to establish interactions. This is achieved by an overlay of standard finite elements with negligible stiffness, provided in the ABAQUS element library, with polyhedral user elements. By means of several numerical examples, the advantages of polyhedral elements regarding the treatment of non-matching interfaces and automatic mesh generation are clearly demonstrated. Thus, the performance of ABAQUS for problems involving interfaces is augmented based on the availability of polyhedral meshes. Due to the implementation of polyhedral user elements, ABAQUS can directly handle complex geometries given in the form of digital images or stereolithography (STL) files. In order to facilitate the use of the proposed approach, the code of the UEL is published open-source and can be downloaded from https://github.com/ShukaiYa/SBFEM-UEL.
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation
Venue: arXiv cs.CV 2021
Year: 2021

High-resolution remote sensing images can provide abundant appearance information for ship detection. Although several existing methods use image super-resolution (SR) approaches to improve the detection performance, they consider image SR and ship detection as two separate processes and overlook the internal coherence between these two correlated tasks. In this paper, we explore the potential benefits introduced by image SR to ship detection, and propose an end-to-end network named ShipSRDet. In our method, we not only feed the super-resolved images to the detector but also integrate the intermediate features of the SR network with those of the detection network. In this way, the informative feature representation extracted by the SR network can be fully used for ship detection. Experimental results on the HRSC dataset validate the effectiveness of our method. Our ShipSRDet can recover the missing details from the input image and achieves promising ship detection performance.
Understanding Barriers to Internal Startups in Large Organizations: Evidence from a Globally Distributed Company
Venue: arXiv cs.SE 2021
Year: 2021

Large global companies need to speed up their innovation activities to increase competitive advantage. However, such companies' organizational structures impede their ability to capture trends they are well aware of due to bureaucracy, slow decision-making, distributed departments, and distributed processes. One way to strengthen the innovation capability is through fostering internal startups. We report findings from an embedded multiple-case study of five internal startups in a globally distributed company to identify barriers for software product innovation: late involvement of software developers, executive sponsor is missing or not clarified, yearly budgeting and planning, unclear decision-making authority, lack of digital infrastructure for experimentation and access to data from external actors. Drawing on the framework of continuous software engineering proposed by Fitzgerald and Stol, we discuss the role of BizDev in software product innovation. We suggest that lack of continuity, rather than the lack of speed, is an ultimate challenge for internal startups in large global companies.
What's in My LiDAR Odometry Toolbox?
Venue: arXiv cs.RO 2021
Year: 2021

With the democratization of 3D LiDAR sensors, precise LiDAR odometries and SLAM are in high demand. New methods regularly appear, proposing solutions ranging from small variations in classical algorithms to radically new paradigms based on deep learning. Yet it is often difficult to compare these methods, notably due to the few datasets on which the methods can be evaluated and compared. Furthermore, their weaknesses are rarely examined, often letting the user discover the hard way whether a method would be appropriate for a use case. In this paper, we review and organize the main 3D LiDAR odometries into distinct categories. We implemented several approaches (geometric based, deep learning based, and hybrid methods) to conduct an in-depth analysis of their strengths and weaknesses on multiple datasets, guiding the reader through the different LiDAR odometries available. Implementation of the methods has been made publicly available at https://github.com/Kitware/pyLiDAR-SLAM.
The Human Evaluation Datasheet 1.0: A Template for Recording Details of Human Evaluation Experiments in NLP
Venue: arXiv cs.CL 2021
Year: 2021

This paper introduces the Human Evaluation Datasheet, a template for recording the details of individual human evaluation experiments in Natural Language Processing (NLP). Originally taking inspiration from seminal papers by Bender and Friedman (2018), Mitchell et al. (2019), and Gebru et al. (2020), the Human Evaluation Datasheet is intended to facilitate the recording of properties of human evaluations in sufficient detail, and with sufficient standardisation, to support comparability, meta-evaluation, and reproducibility tests.
Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss
Venue: arXiv cs.CR 2021
Year: 2021

Cyber attacks pose crucial threats to computer system security, and put digital treasuries at excessive risks. This leads to an urgent call for an effective intrusion detection system that can identify the intrusion attacks with high accuracy. It is challenging to classify the intrusion events due to the wide variety of attacks. Furthermore, in a normal network environment, a majority of the connections are initiated by benign behaviors. The class imbalance issue in intrusion detection forces the classifier to be biased toward the majority/benign class, thus leave many attack incidents undetected. Spurred by the success of deep neural networks in computer vision and natural language processing, in this paper, we design a new system named DeepIDEA that takes full advantage of deep learning to enable intrusion detection and classification. To achieve high detection accuracy on imbalanced data, we design a novel attack-sharing loss function that can effectively move the decision boundary towards the attack classes and eliminates the bias towards the majority/benign class. By using this loss function, DeepIDEA respects the fact that the intrusion mis-classification should receive higher penalty than the attack mis-classification. Extensive experimental results on three benchmark datasets demonstrate the high detection accuracy of DeepIDEA. In particular, compared with eight state-of-the-art approaches, DeepIDEA always provides the best class-balanced accuracy.
Interpretable Distance Metric Learning for Handwritten Chinese Character Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Handwriting recognition is of crucial importance to both Human Computer Interaction (HCI) and paperwork digitization. In the general field of Optical Character Recognition (OCR), handwritten Chinese character recognition faces tremendous challenges due to the enormously large character sets and the amazing diversity of writing styles. Learning an appropriate distance metric to measure the difference between data inputs is the foundation of accurate handwritten character recognition. Existing distance metric learning approaches either produce unacceptable error rates, or provide little interpretability in the results. In this paper, we propose an interpretable distance metric learning approach for handwritten Chinese character recognition. The learned metric is a linear combination of intelligible base metrics, and thus provides meaningful insights to ordinary users. Our experimental results on a benchmark dataset demonstrate the superior efficiency, accuracy and interpretability of our proposed approach.
Few-Shot Visual Grounding for Natural Human-Robot Interaction
Venue: arXiv cs.CV 2021
Year: 2021

Natural Human-Robot Interaction (HRI) is one of the key components for service robots to be able to work in human-centric environments. In such dynamic environments, the robot needs to understand the intention of the user to accomplish a task successfully. Towards addressing this point, we propose a software architecture that segments a target object from a crowded scene, indicated verbally by a human user. At the core of our system, we employ a multi-modal deep neural network for visual grounding. Unlike most grounding methods that tackle the challenge using pre-trained object detectors via a two-stepped process, we develop a single stage zero-shot model that is able to provide predictions in unseen data. We evaluate the performance of the proposed model on real RGB-D data collected from public scene datasets. Experimental results showed that the proposed model performs well in terms of accuracy and speed, while showcasing robustness to variation in the natural language input.
Moderate Density Parity-Check Codes from Projective Bundles
Venue: arXiv cs.IT 2021
Year: 2021

A new construction for moderate density parity-check (MDPC) codes using finite geometry is proposed. We design a parity-check matrix for this family of binary codes as the concatenation of two matrices: the incidence matrix between points and lines of the Desarguesian projective plane and the incidence matrix between points and ovals of a projective bundle. A projective bundle is a special collection of ovals which pairwise meet in a unique point. We determine minimum distance and dimension of these codes, showing that they have a natural quasi-cyclic structure. In addition, we analyze the error-correction performance within one round of a modification of Gallager's bit-flipping decoding algorithm. In this setting, our codes have the best possible error-correction performance for this range of parameters.
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
Venue: arXiv cs.LG 2021
Year: 2021

The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies presents a significant barrier to deploying neural network-based control in autonomous vehicles. In this paper, we present a reinforcement learning based approach to autonomous vehicle longitudinal control, where the rule-based safety cages provide enhanced safety for the vehicle as well as weak supervision to the reinforcement learning agent. By guiding the agent to meaningful states and actions, this weak supervision improves the convergence during training and enhances the safety of the final trained policy. This rule-based supervisory controller has the further advantage of being fully interpretable, thereby enabling traditional validation and verification approaches to ensure the safety of the vehicle. We compare models with and without safety cages, as well as models with optimal and constrained model parameters, and show that the weak supervision consistently improves the safety of exploration, speed of convergence, and model performance. Additionally, we show that when the model parameters are constrained or sub-optimal, the safety cages can enable a model to learn a safe driving policy even when the model could not be trained to drive through reinforcement learning alone.
On additive spanners in weighted graphs with local error
Venue: arXiv cs.DM 2021
Year: 2021

An \emph{additive $+β$ spanner} of a graph $G$ is a subgraph which preserves distances up to an additive $+β$ error. Additive spanners are well-studied in unweighted graphs but have only recently received attention in weighted graphs [Elkin et al.\ 2019 and 2020, Ahmed et al.\ 2020]. This paper makes two new contributions to the theory of weighted additive spanners. For weighted graphs, [Ahmed et al.\ 2020] provided constructions of sparse spanners with \emph{global} error $β= cW$, where $W$ is the maximum edge weight in $G$ and $c$ is constant. We improve these to \emph{local} error by giving spanners with additive error $+cW(s,t)$ for each vertex pair $(s,t)$, where $W(s, t)$ is the maximum edge weight along the shortest $s$--$t$ path in $G$. These include pairwise $+(2+\eps)W(\cdot,\cdot)$ and $+(6+\eps) W(\cdot, \cdot)$ spanners over vertex pairs $\Pc \subseteq V \times V$ on $O_{\eps}(n|\Pc|^{1/3})$ and $O_{\eps}(n|\Pc|^{1/4})$ edges for all $\eps > 0$, which extend previously known unweighted results up to $\eps$ dependence, as well as an all-pairs $+4W(\cdot,\cdot)$ spanner on $\widetilde{O}(n^{7/5})$ edges. Besides sparsity, another natural way to measure the quality of a spanner in weighted graphs is by its \emph{lightness}, defined as the total edge weight of the spanner divided by the weight of an MST of $G$. We provide a $+\eps W(\cdot,\cdot)$ spanner with $O_{\eps}(n)$ lightness, and a $+(4+\eps) W(\cdot,\cdot)$ spanner with $O_{\eps}(n^{2/3})$ lightness. These are the first known additive spanners with nontrivial lightness guarantees. All of the above spanners can be constructed in polynomial time.
Intelligent Reflecting Surface Enabled Random Rotations Scheme for the MISO Broadcast Channel
Venue: arXiv cs.IT 2021
Year: 2021

The current literature on intelligent reflecting surface (IRS) focuses on optimizing the IRS phase shifts to yield coherent beamforming gains, under the assumption of perfect channel state information (CSI) of individual IRS-assisted links, which is highly impractical. This work, instead, considers the random rotations scheme at the IRS in which the reflecting elements only employ random phase rotations without requiring any CSI. The only CSI then needed is at the base station (BS) of the overall channel to implement the beamforming transmission scheme. Under this framework, we derive the sum-rate scaling laws in the large number of users regime for the IRS-assisted multiple-input single-output (MISO) broadcast channel, with optimal dirty paper coding (DPC) scheme and the lower-complexity random beamforming (RBF) and deterministic beamforming (DBF) schemes at the BS. The random rotations scheme increases the sum-rate by exploiting multi-user diversity, but also compromises the gain to some extent due to correlation. Finally, energy efficiency maximization problems in terms of the number of BS antennas, IRS elements and transmit power are solved using the derived scaling laws. Simulation results show the proposed scheme to improve the sum-rate, with performance becoming close to that under coherent beamforming for a large number of users.
Fused Deep Features Based Classification Framework for COVID-19 Classification with Optimized MLP
Venue: arXiv cs.LG 2021
Year: 2021

The new type of Coronavirus disease called COVID-19 continues to spread quite rapidly. Although it shows some specific symptoms, this disease, which can show different symptoms in almost every individual, has caused hundreds of thousands of patients to die. Although healthcare professionals work hard to prevent further loss of life, the rate of disease spread is very high. For this reason, the help of computer aided diagnosis (CAD) and artificial intelligence (AI) algorithms is vital. In this study, a method based on optimization of convolutional neural network (CNN) architecture, which is the most effective image analysis method of today, is proposed to fulfill the mentioned COVID-19 detection needs. First, COVID-19 images are trained using ResNet-50 and VGG-16 architectures. Then, features in the last layer of these two architectures are combined with feature fusion. These new image features matrices obtained with feature fusion are classified for COVID detection. A multi-layer perceptron (MLP) structure optimized by the whale optimization algorithm is used for the classification process. The obtained results show that the performance of the proposed framework is almost 4.5% higher than VGG-16 performance and almost 3.5% higher than ResNet-50 performance.
Releasing Locks As Early As You Can: Reducing Contention of Hotspots by Violating Two-Phase Locking (Extended Version)
Venue: arXiv cs.DB 2021
Year: 2021

Hotspots, a small set of tuples frequently read/written by a large number of transactions, cause contention in a concurrency control protocol. While a hotspot may comprise only a small fraction of a transaction's execution time, conventional strict two-phase locking allows a transaction to release lock only after the transaction completes, which leaves significant parallelism unexploited. Ideally, a concurrency control protocol serializes transactions only for the duration of the hotspots, rather than the duration of transactions. We observe that exploiting such parallelism requires violating two-phase locking. In this paper, we propose Bamboo, a new concurrency control protocol that can enable such parallelism by modifying the conventional two-phase locking, while maintaining the same guarantees in correctness. We thoroughly analyzed the effect of cascading aborts involved in reading uncommitted data and discussed optimizations that can be applied to further improve the performance. Our evaluation on TPC-C shows a performance improvement up to 4x compared to the best of pessimistic and optimistic baseline protocols. On synthetic workloads that contain a single hotspot, Bamboo achieves a speedup up to 19x over baselines.